Cucurbitacin E Has Neuroprotective Properties and Autophagic Modulating Activities on Dopaminergic Neurons
Bibliographic record
Abstract
Natural molecules are under intensive study for their potential as preventive and/or adjuvant therapies for neurodegenerative disorders such as Parkinson's disease (PD). We evaluated the neuroprotective potential of cucurbitacin E (CuE), a tetracyclic triterpenoid phytosterol extracted from the Ecballium elaterium (Cucurbitaceae), using a known cellular model of PD, NGF-differentiated PC12. In our postmitotic experimental paradigm, neuronal cells were treated with the parkinsonian toxin 1-methyl-4-phenylpyridinium (MPP(+)) to provoke significant cellular damage and apoptosis or with the potent N,N-diethyldithiocarbamate (DDC) to induce superoxide (O2(•-)) production, and CuE was administered prior to and during the neurotoxic treatment. We measured cellular death and reactive oxygen species to evaluate the antioxidant and antiapoptotic properties of CuE. In addition, we analyzed cellular macroautophagy, a bulk degradation process involving the lysosomal pathway. CuE showed neuroprotective effects on MPP(+)-induced cell death. However, CuE failed to rescue neuronal cells from oxidative stress induced by MPP(+) or DDC. Microscopy and western blot data show an intriguing involvement of CuE in maintaining lysosomal distribution and decreasing autophagy flux. Altogether, these data indicate that CuE decreases neuronal death and autophagic flux in a postmitotic cellular model of PD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".